Canola yield improvement on the Canadian Prairies from 2000 to 2013
Bibliographic record
Abstract
During the period from 2000 to 2013, average canola yields from Canadian farms increased from 1330 to 2025 kg ha–1, or 54 kg ha–1 year–1. The objective of this review was to propose likely reasons behind this increase by examining genotypic, environmental and agronomic factors. During this period, hybrid canola cultivars with herbicide tolerance (HY-HT) expanded from 80% to >95% of the area sown to canola. Genetic gain from switching from open-pollinated cultivars to HY-HT cultivars was estimated to account for 32 kg ha–1 year–1. When some key environmental factors were examined, there were no significant linear changes in growing season temperature, although the linear increase in April and May precipitation was significant and likely responsible for an increase of 12 kg ha–1 year–1. When coupled with the yield increase from changes in atmospheric CO2 (3 kg ha–1 year–1), the environment was estimated to account for ~15 kg ha–1 year–1. Ignoring all main-factor interactions, changes due to management accounted for the remainder, or 7 kg ha–1 year–1. The expanded use of HY-HT varieties has resulted in better weed control, and an increase in the use of minimum tillage, leading to greater water-use efficiency and higher yield. It is likely that many of the effects of changes in management were hidden in the interaction with genotype and environment main effects. It is difficult to estimate these interactions without designing experiments to do so. The design and implementation of experiments to understand the interaction among main factors should be a priority. Future yield targets of 25 Mt canola by 2025 will require an increase in yield per ha beyond the current rate, or an increase in the land seeded to canola, or a combination of the two factors. Continued progress with canola yield depends on active plant-breeding programs, agronomic research using new varieties, favourable environmental conditions, and high world commodity prices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".